Overview

Dataset statistics

Number of variables26
Number of observations10526
Missing cells31577
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory208.0 B

Variable types

Categorical7
Numeric16
DateTime1
Unsupported2

Alerts

State has constant value ""Constant
Unnamed: 25 has constant value ""Constant
Borough is highly overall correlated with City and 3 other fieldsHigh correlation
City is highly overall correlated with Borough and 4 other fieldsHigh correlation
Dewpot_Avg is highly overall correlated with Dewpot_M and 4 other fieldsHigh correlation
Dewpot_M is highly overall correlated with Dewpot_Avg and 5 other fieldsHigh correlation
Dewpot_Max is highly overall correlated with Dewpot_Avg and 4 other fieldsHigh correlation
Pressure_M is highly overall correlated with Pressure_MaxHigh correlation
Pressure_Max is highly overall correlated with Pressure_MHigh correlation
Station is highly overall correlated with Borough and 6 other fieldsHigh correlation
Wdspeed_Avg is highly overall correlated with City and 3 other fieldsHigh correlation
Wdspeed_Max is highly overall correlated with Station and 2 other fieldsHigh correlation
humidity_avg is highly overall correlated with Dewpot_M and 3 other fieldsHigh correlation
humidity_m is highly overall correlated with humidity_avg and 1 other fieldsHigh correlation
humidity_max is highly overall correlated with humidity_avg and 2 other fieldsHigh correlation
latitude is highly overall correlated with Borough and 3 other fieldsHigh correlation
longitude is highly overall correlated with Borough and 3 other fieldsHigh correlation
precipitation_total is highly overall correlated with humidity_avg and 1 other fieldsHigh correlation
temperature_avg is highly overall correlated with Dewpot_Avg and 4 other fieldsHigh correlation
temperature_m is highly overall correlated with Dewpot_Avg and 4 other fieldsHigh correlation
temperature_max is highly overall correlated with Dewpot_Avg and 4 other fieldsHigh correlation
zipcode is highly overall correlated with Station and 2 other fieldsHigh correlation
Unnamed: 23 has 10526 (100.0%) missing valuesMissing
Unnamed: 24 has 10526 (100.0%) missing valuesMissing
Unnamed: 25 has 10525 (> 99.9%) missing valuesMissing
Pressure_M is highly skewed (γ1 = -34.41313394)Skewed
Station is uniformly distributedUniform
latitude is uniformly distributedUniform
longitude is uniformly distributedUniform
Borough is uniformly distributedUniform
City is uniformly distributedUniform
Unnamed: 23 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 24 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Wdspeed_Avg has 505 (4.8%) zerosZeros
Wdspeed_M has 9827 (93.4%) zerosZeros
precipitation_total has 6528 (62.0%) zerosZeros

Reproduction

Analysis started2023-11-28 16:24:52.156508
Analysis finished2023-11-28 16:25:16.255099
Duration24.1 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Station
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
KNYBRONX14
2107 
KNYNEWYO103
2107 
KNYNEWYO343
2106 
KNYJACKS2
2106 
KNYBROOK54
2100 

Length

Max length11
Median length10
Mean length10.200171
Min length9

Characters and Unicode

Total characters107367
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKNYBRONX14
2nd rowKNYBRONX14
3rd rowKNYBRONX14
4th rowKNYBRONX14
5th rowKNYBRONX14

Common Values

ValueCountFrequency (%)
KNYBRONX14 2107
20.0%
KNYNEWYO103 2107
20.0%
KNYNEWYO343 2106
20.0%
KNYJACKS2 2106
20.0%
KNYBROOK54 2100
20.0%

Length

2023-11-28T11:25:16.318334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:16.420737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
knybronx14 2107
20.0%
knynewyo103 2107
20.0%
knynewyo343 2106
20.0%
knyjacks2 2106
20.0%
knybrook54 2100
20.0%

Most occurring characters

ValueCountFrequency (%)
N 16846
15.7%
Y 14739
13.7%
K 14732
13.7%
O 10520
9.8%
3 6319
 
5.9%
4 6313
 
5.9%
1 4214
 
3.9%
W 4213
 
3.9%
E 4213
 
3.9%
R 4207
 
3.9%
Other values (9) 21051
19.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84208
78.4%
Decimal Number 23159
 
21.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 16846
20.0%
Y 14739
17.5%
K 14732
17.5%
O 10520
12.5%
W 4213
 
5.0%
E 4213
 
5.0%
R 4207
 
5.0%
B 4207
 
5.0%
X 2107
 
2.5%
J 2106
 
2.5%
Other values (3) 6318
 
7.5%
Decimal Number
ValueCountFrequency (%)
3 6319
27.3%
4 6313
27.3%
1 4214
18.2%
0 2107
 
9.1%
2 2106
 
9.1%
5 2100
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 84208
78.4%
Common 23159
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 16846
20.0%
Y 14739
17.5%
K 14732
17.5%
O 10520
12.5%
W 4213
 
5.0%
E 4213
 
5.0%
R 4207
 
5.0%
B 4207
 
5.0%
X 2107
 
2.5%
J 2106
 
2.5%
Other values (3) 6318
 
7.5%
Common
ValueCountFrequency (%)
3 6319
27.3%
4 6313
27.3%
1 4214
18.2%
0 2107
 
9.1%
2 2106
 
9.1%
5 2100
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 16846
15.7%
Y 14739
13.7%
K 14732
13.7%
O 10520
9.8%
3 6319
 
5.9%
4 6313
 
5.9%
1 4214
 
3.9%
W 4213
 
3.9%
E 4213
 
3.9%
R 4207
 
3.9%
Other values (9) 21051
19.6%

latitude
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
40.8616
2107 
40.5674
2107 
40.7638
2106 
40.7557
2106 
40.6215
2100 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters73682
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40.8616
2nd row40.8616
3rd row40.8616
4th row40.8616
5th row40.8616

Common Values

ValueCountFrequency (%)
40.8616 2107
20.0%
40.5674 2107
20.0%
40.7638 2106
20.0%
40.7557 2106
20.0%
40.6215 2100
20.0%

Length

2023-11-28T11:25:16.503129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:16.588736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
40.8616 2107
20.0%
40.5674 2107
20.0%
40.7638 2106
20.0%
40.7557 2106
20.0%
40.6215 2100
20.0%

Most occurring characters

ValueCountFrequency (%)
4 12633
17.1%
6 10527
14.3%
0 10526
14.3%
. 10526
14.3%
7 8425
11.4%
5 8419
11.4%
8 4213
 
5.7%
1 4207
 
5.7%
3 2106
 
2.9%
2 2100
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63156
85.7%
Other Punctuation 10526
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 12633
20.0%
6 10527
16.7%
0 10526
16.7%
7 8425
13.3%
5 8419
13.3%
8 4213
 
6.7%
1 4207
 
6.7%
3 2106
 
3.3%
2 2100
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 10526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 12633
17.1%
6 10527
14.3%
0 10526
14.3%
. 10526
14.3%
7 8425
11.4%
5 8419
11.4%
8 4213
 
5.7%
1 4207
 
5.7%
3 2106
 
2.9%
2 2100
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 12633
17.1%
6 10527
14.3%
0 10526
14.3%
. 10526
14.3%
7 8425
11.4%
5 8419
11.4%
8 4213
 
5.7%
1 4207
 
5.7%
3 2106
 
2.9%
2 2100
 
2.9%

longitude
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
-73.8809
2107 
-74.1343
2107 
-73.9918
2106 
-73.8831
2106 
-74.0096
2100 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters84208
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-73.8809
2nd row-73.8809
3rd row-73.8809
4th row-73.8809
5th row-73.8809

Common Values

ValueCountFrequency (%)
-73.8809 2107
20.0%
-74.1343 2107
20.0%
-73.9918 2106
20.0%
-73.8831 2106
20.0%
-74.0096 2100
20.0%

Length

2023-11-28T11:25:16.668958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:16.754705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
73.8809 2107
20.0%
74.1343 2107
20.0%
73.9918 2106
20.0%
73.8831 2106
20.0%
74.0096 2100
20.0%

Most occurring characters

ValueCountFrequency (%)
3 12639
15.0%
8 10532
12.5%
- 10526
12.5%
7 10526
12.5%
. 10526
12.5%
9 8419
10.0%
1 6319
7.5%
4 6314
7.5%
0 6307
7.5%
6 2100
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63156
75.0%
Dash Punctuation 10526
 
12.5%
Other Punctuation 10526
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12639
20.0%
8 10532
16.7%
7 10526
16.7%
9 8419
13.3%
1 6319
10.0%
4 6314
10.0%
0 6307
10.0%
6 2100
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 10526
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84208
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12639
15.0%
8 10532
12.5%
- 10526
12.5%
7 10526
12.5%
. 10526
12.5%
9 8419
10.0%
1 6319
7.5%
4 6314
7.5%
0 6307
7.5%
6 2100
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12639
15.0%
8 10532
12.5%
- 10526
12.5%
7 10526
12.5%
. 10526
12.5%
9 8419
10.0%
1 6319
7.5%
4 6314
7.5%
0 6307
7.5%
6 2100
 
2.5%

Borough
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Bronx
2107 
Staten Island
2107 
Manhattan
2106 
Queens
2106 
Brooklyn
2100 

Length

Max length13
Median length8
Mean length8.200266
Min length5

Characters and Unicode

Total characters86316
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBronx
2nd rowBronx
3rd rowBronx
4th rowBronx
5th rowBronx

Common Values

ValueCountFrequency (%)
Bronx 2107
20.0%
Staten Island 2107
20.0%
Manhattan 2106
20.0%
Queens 2106
20.0%
Brooklyn 2100
20.0%

Length

2023-11-28T11:25:16.841685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:16.935684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bronx 2107
16.7%
staten 2107
16.7%
island 2107
16.7%
manhattan 2106
16.7%
queens 2106
16.7%
brooklyn 2100
16.6%

Most occurring characters

ValueCountFrequency (%)
n 14739
17.1%
a 10532
12.2%
t 8426
9.8%
e 6319
 
7.3%
o 6307
 
7.3%
s 4213
 
4.9%
B 4207
 
4.9%
r 4207
 
4.9%
l 4207
 
4.9%
d 2107
 
2.4%
Other values (10) 21052
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71576
82.9%
Uppercase Letter 12633
 
14.6%
Space Separator 2107
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 14739
20.6%
a 10532
14.7%
t 8426
11.8%
e 6319
8.8%
o 6307
8.8%
s 4213
 
5.9%
r 4207
 
5.9%
l 4207
 
5.9%
d 2107
 
2.9%
x 2107
 
2.9%
Other values (4) 8412
11.8%
Uppercase Letter
ValueCountFrequency (%)
B 4207
33.3%
I 2107
16.7%
S 2107
16.7%
M 2106
16.7%
Q 2106
16.7%
Space Separator
ValueCountFrequency (%)
2107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84209
97.6%
Common 2107
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 14739
17.5%
a 10532
12.5%
t 8426
10.0%
e 6319
 
7.5%
o 6307
 
7.5%
s 4213
 
5.0%
B 4207
 
5.0%
r 4207
 
5.0%
l 4207
 
5.0%
d 2107
 
2.5%
Other values (9) 18945
22.5%
Common
ValueCountFrequency (%)
2107
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 14739
17.1%
a 10532
12.2%
t 8426
9.8%
e 6319
 
7.3%
o 6307
 
7.3%
s 4213
 
4.9%
B 4207
 
4.9%
r 4207
 
4.9%
l 4207
 
4.9%
d 2107
 
2.4%
Other values (10) 21052
24.4%

City
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Botanical Garden
2107 
Richmondtown
2107 
New York
2106 
Jackson Heights
2106 
Dyker Heights
2100 

Length

Max length16
Median length13
Mean length12.800114
Min length8

Characters and Unicode

Total characters134734
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBotanical Garden
2nd rowBotanical Garden
3rd rowBotanical Garden
4th rowBotanical Garden
5th rowBotanical Garden

Common Values

ValueCountFrequency (%)
Botanical Garden 2107
20.0%
Richmondtown 2107
20.0%
New York 2106
20.0%
Jackson Heights 2106
20.0%
Dyker Heights 2100
20.0%

Length

2023-11-28T11:25:17.023311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:17.118344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
heights 4206
22.2%
botanical 2107
11.1%
garden 2107
11.1%
richmondtown 2107
11.1%
new 2106
11.1%
york 2106
11.1%
jackson 2106
11.1%
dyker 2100
11.1%

Most occurring characters

ValueCountFrequency (%)
n 10534
 
7.8%
o 10533
 
7.8%
e 10519
 
7.8%
a 8427
 
6.3%
t 8420
 
6.2%
i 8420
 
6.2%
8419
 
6.2%
c 6320
 
4.7%
h 6313
 
4.7%
r 6313
 
4.7%
Other values (16) 50516
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107370
79.7%
Uppercase Letter 18945
 
14.1%
Space Separator 8419
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10534
9.8%
o 10533
9.8%
e 10519
9.8%
a 8427
 
7.8%
t 8420
 
7.8%
i 8420
 
7.8%
c 6320
 
5.9%
h 6313
 
5.9%
r 6313
 
5.9%
s 6312
 
5.9%
Other values (7) 25259
23.5%
Uppercase Letter
ValueCountFrequency (%)
H 4206
22.2%
B 2107
11.1%
R 2107
11.1%
G 2107
11.1%
Y 2106
11.1%
N 2106
11.1%
J 2106
11.1%
D 2100
11.1%
Space Separator
ValueCountFrequency (%)
8419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126315
93.8%
Common 8419
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10534
 
8.3%
o 10533
 
8.3%
e 10519
 
8.3%
a 8427
 
6.7%
t 8420
 
6.7%
i 8420
 
6.7%
c 6320
 
5.0%
h 6313
 
5.0%
r 6313
 
5.0%
s 6312
 
5.0%
Other values (15) 44204
35.0%
Common
ValueCountFrequency (%)
8419
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10534
 
7.8%
o 10533
 
7.8%
e 10519
 
7.8%
a 8427
 
6.3%
t 8420
 
6.2%
i 8420
 
6.2%
8419
 
6.2%
c 6320
 
4.7%
h 6313
 
4.7%
r 6313
 
4.7%
Other values (16) 50516
37.5%

State
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
NY
10526 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters21052
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowNY
3rd rowNY
4th rowNY
5th rowNY

Common Values

ValueCountFrequency (%)
NY 10526
100.0%

Length

2023-11-28T11:25:17.201555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:17.275739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ny 10526
100.0%

Most occurring characters

ValueCountFrequency (%)
N 10526
50.0%
Y 10526
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21052
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 10526
50.0%
Y 10526
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21052
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 10526
50.0%
Y 10526
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 10526
50.0%
Y 10526
50.0%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10676.083
Minimum10018
Maximum11372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:17.330198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10018
5-th percentile10018
Q110306
median10458
Q311228
95-th percentile11372
Maximum11372
Range1354
Interquartile range (IQR)922

Descriptive statistics

Standard deviation530.32058
Coefficient of variation (CV)0.049673705
Kurtosis-1.6451312
Mean10676.083
Median Absolute Deviation (MAD)440
Skewness0.21294881
Sum1.1237645 × 108
Variance281239.92
MonotonicityNot monotonic
2023-11-28T11:25:17.404675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10458 2107
20.0%
10018 2106
20.0%
11372 2106
20.0%
11228 2100
20.0%
10306 1828
17.4%
10308 279
 
2.7%
ValueCountFrequency (%)
10018 2106
20.0%
10306 1828
17.4%
10308 279
 
2.7%
10458 2107
20.0%
11228 2100
20.0%
11372 2106
20.0%
ValueCountFrequency (%)
11372 2106
20.0%
11228 2100
20.0%
10458 2107
20.0%
10308 279
 
2.7%
10306 1828
17.4%
10018 2106
20.0%

wdate
Date

Distinct2107
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Minimum2016-01-01 00:00:00
Maximum2021-10-07 00:00:00
2023-11-28T11:25:17.597548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:17.695193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temperature_max
Real number (ℝ)

HIGH CORRELATION 

Distinct786
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.483574
Minimum12.9
Maximum101.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:17.801161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.9
5-th percentile35.1
Q149.1
median65.4
Q380.6
95-th percentile91.6
Maximum101.2
Range88.3
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.394022
Coefficient of variation (CV)0.28525128
Kurtosis-1.0113355
Mean64.483574
Median Absolute Deviation (MAD)15.7
Skewness-0.1668509
Sum678754.1
Variance338.34004
MonotonicityNot monotonic
2023-11-28T11:25:17.895690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 52
 
0.5%
82 44
 
0.4%
72.9 44
 
0.4%
82.8 43
 
0.4%
46 42
 
0.4%
61 42
 
0.4%
88.3 42
 
0.4%
85.5 41
 
0.4%
77.5 39
 
0.4%
49.6 39
 
0.4%
Other values (776) 10098
95.9%
ValueCountFrequency (%)
12.9 1
 
< 0.1%
13.3 1
 
< 0.1%
13.8 1
 
< 0.1%
14 1
 
< 0.1%
14.2 1
 
< 0.1%
14.4 1
 
< 0.1%
15.3 1
 
< 0.1%
15.4 3
< 0.1%
15.7 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
101.2 1
< 0.1%
101 1
< 0.1%
100.4 1
< 0.1%
100.3 1
< 0.1%
100 1
< 0.1%
99.8 1
< 0.1%
99.5 2
< 0.1%
99.3 1
< 0.1%
99.2 2
< 0.1%
99.1 2
< 0.1%

temperature_avg
Real number (ℝ)

HIGH CORRELATION 

Distinct762
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.924634
Minimum7.3
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:17.991466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile29.525
Q142.6
median57
Q372.5
95-th percentile81.8
Maximum92
Range84.7
Interquartile range (IQR)29.9

Descriptive statistics

Standard deviation17.1851
Coefficient of variation (CV)0.30189214
Kurtosis-1.0039705
Mean56.924634
Median Absolute Deviation (MAD)15
Skewness-0.15224912
Sum599188.7
Variance295.32765
MonotonicityNot monotonic
2023-11-28T11:25:18.081240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.5 40
 
0.4%
76.2 37
 
0.4%
71.7 34
 
0.3%
49.1 33
 
0.3%
74.6 33
 
0.3%
73.5 33
 
0.3%
42.1 33
 
0.3%
72.5 32
 
0.3%
75.8 31
 
0.3%
79.1 31
 
0.3%
Other values (752) 10189
96.8%
ValueCountFrequency (%)
7.3 1
< 0.1%
8.4 2
< 0.1%
8.6 1
< 0.1%
8.8 1
< 0.1%
9.3 1
< 0.1%
9.4 1
< 0.1%
9.6 1
< 0.1%
9.7 1
< 0.1%
9.8 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
92 2
< 0.1%
91.8 1
< 0.1%
91.7 1
< 0.1%
91.5 1
< 0.1%
91.4 1
< 0.1%
90.9 1
< 0.1%
90.8 1
< 0.1%
90.2 1
< 0.1%
89.8 1
< 0.1%
89.6 1
< 0.1%

temperature_m
Real number (ℝ)

HIGH CORRELATION 

Distinct740
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.073523
Minimum-2.3
Maximum85.5
Zeros2
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size82.4 KiB
2023-11-28T11:25:18.177850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.3
5-th percentile23.4
Q136.7
median49.6
Q365.5
95-th percentile74.5
Maximum85.5
Range87.8
Interquartile range (IQR)28.8

Descriptive statistics

Standard deviation16.802664
Coefficient of variation (CV)0.33555986
Kurtosis-0.94926296
Mean50.073523
Median Absolute Deviation (MAD)14.3
Skewness-0.14656756
Sum527073.9
Variance282.32953
MonotonicityNot monotonic
2023-11-28T11:25:18.272836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 61
 
0.6%
70.5 55
 
0.5%
71.2 53
 
0.5%
69.1 50
 
0.5%
37 49
 
0.5%
73 45
 
0.4%
72.3 45
 
0.4%
69.3 45
 
0.4%
70.3 44
 
0.4%
36.1 43
 
0.4%
Other values (730) 10036
95.3%
ValueCountFrequency (%)
-2.3 1
 
< 0.1%
-0.6 1
 
< 0.1%
0 2
< 0.1%
0.9 1
 
< 0.1%
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
2 1
 
< 0.1%
2.8 1
 
< 0.1%
3.4 1
 
< 0.1%
3.6 4
< 0.1%
ValueCountFrequency (%)
85.5 1
 
< 0.1%
84.2 2
< 0.1%
84 1
 
< 0.1%
83.1 2
< 0.1%
82.9 2
< 0.1%
82.8 1
 
< 0.1%
82.7 1
 
< 0.1%
82.6 3
< 0.1%
82.4 3
< 0.1%
82.1 1
 
< 0.1%

Dewpot_Max
Real number (ℝ)

HIGH CORRELATION 

Distinct785
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.731237
Minimum-14.1
Maximum87.8
Zeros1
Zeros (%)< 0.1%
Negative18
Negative (%)0.2%
Memory size82.4 KiB
2023-11-28T11:25:18.369767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14.1
5-th percentile20.6
Q137
median52.5
Q366.5
95-th percentile74.8
Maximum87.8
Range101.9
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.742545
Coefficient of variation (CV)0.34973611
Kurtosis-0.69628263
Mean50.731237
Median Absolute Deviation (MAD)14.6
Skewness-0.39902333
Sum533997
Variance314.79792
MonotonicityNot monotonic
2023-11-28T11:25:18.465621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.6 52
 
0.5%
68.4 51
 
0.5%
73 49
 
0.5%
70 48
 
0.5%
68.7 47
 
0.4%
72.3 47
 
0.4%
71.2 47
 
0.4%
68 46
 
0.4%
71.1 46
 
0.4%
71.8 46
 
0.4%
Other values (775) 10047
95.4%
ValueCountFrequency (%)
-14.1 1
< 0.1%
-11.4 1
< 0.1%
-9.4 1
< 0.1%
-6.1 1
< 0.1%
-6 1
< 0.1%
-5.3 1
< 0.1%
-4.9 1
< 0.1%
-4.4 1
< 0.1%
-3.5 1
< 0.1%
-3.2 1
< 0.1%
ValueCountFrequency (%)
87.8 1
< 0.1%
85.8 1
< 0.1%
85.1 1
< 0.1%
84 1
< 0.1%
83.8 1
< 0.1%
82.6 2
< 0.1%
82.4 1
< 0.1%
82.2 1
< 0.1%
81.1 2
< 0.1%
81 1
< 0.1%

Dewpot_Avg
Real number (ℝ)

HIGH CORRELATION 

Distinct811
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.318659
Minimum-18.8
Maximum78.5
Zeros1
Zeros (%)< 0.1%
Negative74
Negative (%)0.7%
Memory size82.4 KiB
2023-11-28T11:25:18.562386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-18.8
5-th percentile12.6
Q129.7
median45.2
Q360.6
95-th percentile70.9
Maximum78.5
Range97.3
Interquartile range (IQR)30.9

Descriptive statistics

Standard deviation18.687459
Coefficient of variation (CV)0.4216612
Kurtosis-0.81267361
Mean44.318659
Median Absolute Deviation (MAD)15.5
Skewness-0.29557406
Sum466498.2
Variance349.22112
MonotonicityNot monotonic
2023-11-28T11:25:18.662012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.6 30
 
0.3%
51 30
 
0.3%
63.7 30
 
0.3%
66.8 30
 
0.3%
56.2 30
 
0.3%
66 29
 
0.3%
68.9 29
 
0.3%
65.7 28
 
0.3%
61.5 28
 
0.3%
29.5 28
 
0.3%
Other values (801) 10234
97.2%
ValueCountFrequency (%)
-18.8 1
< 0.1%
-16.2 1
< 0.1%
-13.9 1
< 0.1%
-12 1
< 0.1%
-11.5 1
< 0.1%
-11.4 1
< 0.1%
-11.1 1
< 0.1%
-10.9 1
< 0.1%
-9.7 1
< 0.1%
-9.2 2
< 0.1%
ValueCountFrequency (%)
78.5 1
 
< 0.1%
77.7 1
 
< 0.1%
76.5 1
 
< 0.1%
76.4 1
 
< 0.1%
76.2 1
 
< 0.1%
76.1 1
 
< 0.1%
76 2
< 0.1%
75.9 3
< 0.1%
75.8 1
 
< 0.1%
75.7 2
< 0.1%

Dewpot_M
Real number (ℝ)

HIGH CORRELATION 

Distinct836
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.771452
Minimum-26.7
Maximum76.1
Zeros5
Zeros (%)< 0.1%
Negative253
Negative (%)2.4%
Memory size82.4 KiB
2023-11-28T11:25:18.761880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-26.7
5-th percentile4.8
Q122.3
median38.7
Q354.6
95-th percentile67.3
Maximum76.1
Range102.8
Interquartile range (IQR)32.3

Descriptive statistics

Standard deviation19.821558
Coefficient of variation (CV)0.52477619
Kurtosis-0.8816216
Mean37.771452
Median Absolute Deviation (MAD)16.2
Skewness-0.20399153
Sum397582.3
Variance392.89417
MonotonicityNot monotonic
2023-11-28T11:25:18.859916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.9 41
 
0.4%
36 41
 
0.4%
28 39
 
0.4%
28.6 37
 
0.4%
39 36
 
0.3%
43.2 36
 
0.3%
21.9 35
 
0.3%
52.7 35
 
0.3%
64 35
 
0.3%
55.2 34
 
0.3%
Other values (826) 10157
96.5%
ValueCountFrequency (%)
-26.7 1
< 0.1%
-21.3 1
< 0.1%
-19.9 1
< 0.1%
-17.8 1
< 0.1%
-17.5 1
< 0.1%
-17 2
< 0.1%
-16.8 1
< 0.1%
-16.4 1
< 0.1%
-15.9 2
< 0.1%
-15.7 1
< 0.1%
ValueCountFrequency (%)
76.1 1
 
< 0.1%
75.2 1
 
< 0.1%
73.6 2
< 0.1%
73.5 1
 
< 0.1%
73.4 2
< 0.1%
73.3 2
< 0.1%
73.2 1
 
< 0.1%
73 1
 
< 0.1%
72.9 3
< 0.1%
72.7 3
< 0.1%

humidity_max
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.31731
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:18.957990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile56
Q173
median86
Q394
95-th percentile98
Maximum100
Range73
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.812467
Coefficient of variation (CV)0.16779541
Kurtosis-0.037361349
Mean82.31731
Median Absolute Deviation (MAD)9
Skewness-0.87109627
Sum866472
Variance190.78425
MonotonicityNot monotonic
2023-11-28T11:25:19.055304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 580
 
5.5%
95 576
 
5.5%
94 562
 
5.3%
93 482
 
4.6%
97 439
 
4.2%
92 430
 
4.1%
91 339
 
3.2%
90 337
 
3.2%
89 309
 
2.9%
87 291
 
2.8%
Other values (61) 6181
58.7%
ValueCountFrequency (%)
27 1
 
< 0.1%
31 1
 
< 0.1%
32 1
 
< 0.1%
33 2
 
< 0.1%
34 3
 
< 0.1%
35 4
 
< 0.1%
36 6
0.1%
37 6
0.1%
38 5
 
< 0.1%
39 13
0.1%
ValueCountFrequency (%)
100 276
2.6%
99 146
 
1.4%
98 192
 
1.8%
97 439
4.2%
96 580
5.5%
95 576
5.5%
94 562
5.3%
93 482
4.6%
92 430
4.1%
91 339
3.2%

humidity_avg
Real number (ℝ)

HIGH CORRELATION 

Distinct204
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.483184
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:19.160589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q154
median66
Q377
95-th percentile90
Maximum100
Range80
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.441311
Coefficient of variation (CV)0.23580575
Kurtosis-0.66162359
Mean65.483184
Median Absolute Deviation (MAD)12
Skewness-0.10913853
Sum689276
Variance238.43409
MonotonicityNot monotonic
2023-11-28T11:25:19.262922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 265
 
2.5%
66 256
 
2.4%
74 246
 
2.3%
64 241
 
2.3%
69 238
 
2.3%
73 234
 
2.2%
67 234
 
2.2%
65 234
 
2.2%
71 231
 
2.2%
61 230
 
2.2%
Other values (194) 8117
77.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
22 2
 
< 0.1%
23 1
 
< 0.1%
24 5
 
< 0.1%
25 7
0.1%
25.9 1
 
< 0.1%
26 3
 
< 0.1%
27 7
0.1%
28 17
0.2%
28.1 1
 
< 0.1%
ValueCountFrequency (%)
100 5
 
< 0.1%
99 2
 
< 0.1%
98 17
 
0.2%
97 27
 
0.3%
96 50
0.5%
95 55
0.5%
94 66
0.6%
93 92
0.9%
92 99
0.9%
91 100
1.0%

humidity_m
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.947653
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:19.370668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q135
median45
Q357
95-th percentile78
Maximum100
Range100
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.672586
Coefficient of variation (CV)0.3551314
Kurtosis-0.20719391
Mean46.947653
Median Absolute Deviation (MAD)11
Skewness0.48408757
Sum494171
Variance277.97512
MonotonicityNot monotonic
2023-11-28T11:25:19.470125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 281
 
2.7%
42 272
 
2.6%
40 268
 
2.5%
34 264
 
2.5%
36 262
 
2.5%
41 257
 
2.4%
44 257
 
2.4%
43 252
 
2.4%
35 249
 
2.4%
33 246
 
2.3%
Other values (85) 7918
75.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 6
 
0.1%
12 9
0.1%
13 15
0.1%
14 12
0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
97 1
 
< 0.1%
96 6
 
0.1%
95 12
0.1%
94 17
0.2%
93 14
0.1%
92 19
0.2%
91 19
0.2%
90 14
0.1%

Wdspeed_Max
Real number (ℝ)

HIGH CORRELATION 

Distinct372
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.373371
Minimum0
Maximum104.5
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:19.571146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16.9
median14
Q319.9
95-th percentile30.6
Maximum104.5
Range104.5
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2534788
Coefficient of variation (CV)0.64379323
Kurtosis2.1202041
Mean14.373371
Median Absolute Deviation (MAD)6
Skewness0.89985722
Sum151294.1
Variance85.62687
MonotonicityNot monotonic
2023-11-28T11:25:19.671494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 401
 
3.8%
13 385
 
3.7%
15 341
 
3.2%
11 320
 
3.0%
19 283
 
2.7%
21 267
 
2.5%
2.9 238
 
2.3%
14 227
 
2.2%
12 227
 
2.2%
2.5 226
 
2.1%
Other values (362) 7611
72.3%
ValueCountFrequency (%)
0 15
 
0.1%
0.2 12
 
0.1%
0.4 27
 
0.3%
0.7 3
 
< 0.1%
0.9 38
 
0.4%
1 1
 
< 0.1%
1.1 65
0.6%
1.3 83
0.8%
1.6 27
 
0.3%
1.8 124
1.2%
ValueCountFrequency (%)
104.5 1
< 0.1%
76.1 1
< 0.1%
74.3 1
< 0.1%
72.9 1
< 0.1%
70.3 1
< 0.1%
66.9 1
< 0.1%
64 1
< 0.1%
61.1 1
< 0.1%
60.2 1
< 0.1%
59.5 1
< 0.1%

Wdspeed_Avg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct186
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4036386
Minimum0
Maximum28
Zeros505
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:19.871707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.6
median2.5
Q35.3
95-th percentile9.9
Maximum28
Range28
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.3231698
Coefficient of variation (CV)0.97635801
Kurtosis2.0471715
Mean3.4036386
Median Absolute Deviation (MAD)2.1
Skewness1.3061319
Sum35826.7
Variance11.043458
MonotonicityNot monotonic
2023-11-28T11:25:19.963395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 814
 
7.7%
0 505
 
4.8%
0.2 501
 
4.8%
0.3 311
 
3.0%
0.4 274
 
2.6%
0.5 216
 
2.1%
0.6 200
 
1.9%
0.7 188
 
1.8%
1.9 163
 
1.5%
0.8 163
 
1.5%
Other values (176) 7191
68.3%
ValueCountFrequency (%)
0 505
4.8%
0.1 814
7.7%
0.2 501
4.8%
0.3 311
 
3.0%
0.4 274
 
2.6%
0.5 216
 
2.1%
0.6 200
 
1.9%
0.7 188
 
1.8%
0.8 163
 
1.5%
0.9 129
 
1.2%
ValueCountFrequency (%)
28 1
< 0.1%
23 2
< 0.1%
22.6 1
< 0.1%
22 1
< 0.1%
21.9 1
< 0.1%
20.3 1
< 0.1%
19.9 1
< 0.1%
19.5 1
< 0.1%
19 1
< 0.1%
18.6 1
< 0.1%

Wdspeed_M
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15508265
Minimum0
Maximum20
Zeros9827
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:20.058366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.9
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8400194
Coefficient of variation (CV)5.4165917
Kurtosis132.53305
Mean0.15508265
Median Absolute Deviation (MAD)0
Skewness9.73721
Sum1632.4
Variance0.7056326
MonotonicityNot monotonic
2023-11-28T11:25:20.149879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 9827
93.4%
0.9 204
 
1.9%
2 146
 
1.4%
1 101
 
1.0%
3 44
 
0.4%
2.9 32
 
0.3%
6 24
 
0.2%
1.3 19
 
0.2%
1.1 17
 
0.2%
7 16
 
0.2%
Other values (25) 96
 
0.9%
ValueCountFrequency (%)
0 9827
93.4%
0.9 204
 
1.9%
1 101
 
1.0%
1.1 17
 
0.2%
1.3 19
 
0.2%
1.6 10
 
0.1%
1.8 9
 
0.1%
2 146
 
1.4%
2.2 3
 
< 0.1%
2.5 2
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
14.8 1
 
< 0.1%
14 3
< 0.1%
13 1
 
< 0.1%
12 3
< 0.1%
10 3
< 0.1%
9 2
< 0.1%
8.1 1
 
< 0.1%

Pressure_Max
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.077114
Minimum28.82
Maximum32.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:20.253297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum28.82
5-th percentile29.56
Q129.94
median30.09
Q330.24
95-th percentile30.48
Maximum32.19
Range3.37
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.26886179
Coefficient of variation (CV)0.0089390821
Kurtosis1.3317876
Mean30.077114
Median Absolute Deviation (MAD)0.15
Skewness-0.48219684
Sum316591.7
Variance0.072286662
MonotonicityNot monotonic
2023-11-28T11:25:20.352772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.09 230
 
2.2%
30.1 222
 
2.1%
30.14 218
 
2.1%
30.11 213
 
2.0%
30.06 200
 
1.9%
30.03 196
 
1.9%
30.08 189
 
1.8%
30.07 189
 
1.8%
30.2 189
 
1.8%
30.16 187
 
1.8%
Other values (172) 8493
80.7%
ValueCountFrequency (%)
28.82 1
 
< 0.1%
28.92 2
< 0.1%
28.99 3
< 0.1%
29.01 1
 
< 0.1%
29.04 1
 
< 0.1%
29.06 1
 
< 0.1%
29.07 1
 
< 0.1%
29.09 2
< 0.1%
29.1 1
 
< 0.1%
29.11 1
 
< 0.1%
ValueCountFrequency (%)
32.19 1
 
< 0.1%
31.61 1
 
< 0.1%
30.86 1
 
< 0.1%
30.85 1
 
< 0.1%
30.84 1
 
< 0.1%
30.82 2
 
< 0.1%
30.77 5
< 0.1%
30.76 3
 
< 0.1%
30.75 7
0.1%
30.74 8
0.1%

Pressure_M
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct213
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.843182
Minimum0
Maximum30.68
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:20.457528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.27
Q129.71
median29.88
Q330.04
95-th percentile30.27
Maximum30.68
Range30.68
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.42080235
Coefficient of variation (CV)0.014100452
Kurtosis2405.7844
Mean29.843182
Median Absolute Deviation (MAD)0.17
Skewness-34.413134
Sum314129.33
Variance0.17707462
MonotonicityNot monotonic
2023-11-28T11:25:20.554869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.92 197
 
1.9%
29.83 197
 
1.9%
29.91 192
 
1.8%
29.85 184
 
1.7%
29.93 183
 
1.7%
29.96 179
 
1.7%
29.88 179
 
1.7%
29.98 177
 
1.7%
29.87 173
 
1.6%
29.9 172
 
1.6%
Other values (203) 8693
82.6%
ValueCountFrequency (%)
0 1
< 0.1%
24.42 1
< 0.1%
26.95 1
< 0.1%
28.01 1
< 0.1%
28.39 1
< 0.1%
28.43 1
< 0.1%
28.48 1
< 0.1%
28.5 2
< 0.1%
28.51 1
< 0.1%
28.53 2
< 0.1%
ValueCountFrequency (%)
30.68 1
 
< 0.1%
30.65 1
 
< 0.1%
30.64 1
 
< 0.1%
30.62 1
 
< 0.1%
30.61 2
 
< 0.1%
30.6 3
< 0.1%
30.59 1
 
< 0.1%
30.58 3
< 0.1%
30.57 5
< 0.1%
30.56 1
 
< 0.1%

precipitation_total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct226
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12535056
Minimum0
Maximum6.84
Zeros6528
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-11-28T11:25:20.657912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.77
Maximum6.84
Range6.84
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.34180889
Coefficient of variation (CV)2.7268238
Kurtosis48.83931
Mean0.12535056
Median Absolute Deviation (MAD)0
Skewness5.4198905
Sum1319.44
Variance0.11683332
MonotonicityNot monotonic
2023-11-28T11:25:20.754908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6528
62.0%
0.01 589
 
5.6%
0.02 262
 
2.5%
0.03 168
 
1.6%
0.04 151
 
1.4%
0.05 141
 
1.3%
0.06 112
 
1.1%
0.07 112
 
1.1%
0.08 97
 
0.9%
0.09 90
 
0.9%
Other values (216) 2276
 
21.6%
ValueCountFrequency (%)
0 6528
62.0%
0.01 589
 
5.6%
0.02 262
 
2.5%
0.03 168
 
1.6%
0.04 151
 
1.4%
0.05 141
 
1.3%
0.06 112
 
1.1%
0.07 112
 
1.1%
0.08 97
 
0.9%
0.09 90
 
0.9%
ValueCountFrequency (%)
6.84 1
< 0.1%
5.72 1
< 0.1%
4.61 1
< 0.1%
4.41 1
< 0.1%
4.4 1
< 0.1%
4.1 1
< 0.1%
3.92 1
< 0.1%
3.8 1
< 0.1%
3.79 1
< 0.1%
3.35 1
< 0.1%

Unnamed: 23
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10526
Missing (%)100.0%
Memory size82.4 KiB

Unnamed: 24
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10526
Missing (%)100.0%
Memory size82.4 KiB

Unnamed: 25
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing10525
Missing (%)> 99.9%
Memory size82.4 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 10525
> 99.9%

Length

2023-11-28T11:25:20.845123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T11:25:20.919211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Interactions

2023-11-28T11:25:14.472362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:54.855425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.296777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.566686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.790421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.023614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.340110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.591426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.846514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.248524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.577610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.834039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.186968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.437877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.776013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.104150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.559773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:54.950469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.380409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.647252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.871473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.107206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.423417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.674805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.933034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.336297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.662150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.916236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.268611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.526855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.862776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.188331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.637088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.032623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.455817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.718619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.944470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.178806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.496965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.748463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.010040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.414530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.735587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.992211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.342823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.606507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.939581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.264382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.712963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.116099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.528534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.787851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.013352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.248657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.568427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.819528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.085440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.490569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.805269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.062487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.412833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.682637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.013913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.337112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.789401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.196791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.603020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.859936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.085182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.320491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.641445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.892264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.162119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.568889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.878580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.137362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.486878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.761630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.091877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.509310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.869909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.276915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.675160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.932467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.155871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.484088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.714054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.965234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.239503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.647193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.951186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.209380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.561662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.840209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.170004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.582640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.948777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.360106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.751994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.007598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.231687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.557541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.789040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.041002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.318473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.727557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.028224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.285904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.638320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.922737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.246666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.660848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.029739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.441018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.828555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.081158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.306639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.631768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.864290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.117099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.400059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.808018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.103253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.361042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.713266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.002486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.327083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.738284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.116159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.530076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.912370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.162335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.388233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.712379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.946360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.201940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.486660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.895077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.188359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.444018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.799075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.090602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.415454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.821863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.203696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.622120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.001143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.244786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.472384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.794824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.031463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.287446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.574893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.982261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.273493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.528298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.883531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.181533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.503773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.908498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.283803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.704608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.081898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.319103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.546501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.868966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.106890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.363280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.654469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.063590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.349384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.604647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.960403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.264232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.587075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.985562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.362099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.786934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.159887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.391612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.622154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:00.942481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.183018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.440188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.828821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.144175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.424601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.679749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.035560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.344528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.668185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.063146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.442032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:55.945424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.237696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.468567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.698369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.017547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.258967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.517251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.908335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.227199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.503183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.756732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.111346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.427019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.749351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.140521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.530204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.036138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.323014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.552249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.783000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.103390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.345707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.603555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:04.995929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.317097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.588239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.843367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.195599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.515760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.838211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.228039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.615506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.123063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.404647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.632834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.862654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.182452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.428448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.684812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.080592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.404573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.671748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:08.924758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.277459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.603104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:12.924405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.309999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:15.696918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:56.208143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:57.484422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:58.708904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:24:59.941365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:01.259647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:02.509207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:03.763805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:05.162855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:06.489201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:07.750526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:09.103809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:10.354977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:11.688798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:13.012926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T11:25:14.389553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-28T11:25:20.989817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BoroughCityDewpot_AvgDewpot_MDewpot_MaxPressure_MPressure_MaxStationWdspeed_AvgWdspeed_MWdspeed_Maxhumidity_avghumidity_mhumidity_maxlatitudelongitudeprecipitation_totaltemperature_avgtemperature_mtemperature_maxzipcode
Borough1.0001.0000.0060.0070.0030.1880.1871.000-0.198-0.027-0.1370.0740.0640.0191.0001.000-0.000-0.019-0.026-0.013-0.100
City1.0001.0000.0160.0140.0150.1590.1611.000-0.519-0.124-0.4560.0790.0660.0481.0001.0000.000-0.012-0.020-0.008-0.500
Dewpot_Avg0.0060.0161.0000.9820.978-0.099-0.3060.043-0.214-0.142-0.1700.4990.4030.4590.0430.0430.2090.9320.9410.904-0.012
Dewpot_M0.0070.0140.9821.0000.937-0.066-0.2970.044-0.217-0.133-0.1830.5030.4480.4250.0440.0440.1800.9070.9270.874-0.007
Dewpot_Max0.0030.0150.9780.9371.000-0.133-0.3040.039-0.198-0.142-0.1460.4440.3260.4660.0390.0390.2190.9310.9290.914-0.019
Pressure_M0.1880.159-0.099-0.066-0.1331.0000.8390.000-0.0740.002-0.119-0.204-0.119-0.3310.0000.000-0.328-0.044-0.039-0.0480.069
Pressure_Max0.1870.161-0.306-0.297-0.3040.8391.0000.2120.0120.031-0.016-0.194-0.120-0.2830.2120.212-0.221-0.280-0.282-0.2750.064
Station1.0001.0000.0430.0440.0390.0000.2121.000-0.632-0.123-0.5980.0020.016-0.0431.0001.000-0.0050.0050.013-0.008-0.600
Wdspeed_Avg-0.198-0.519-0.214-0.217-0.198-0.0740.012-0.6321.0000.3910.890-0.202-0.161-0.1990.3640.364-0.012-0.160-0.163-0.1550.749
Wdspeed_M-0.027-0.124-0.142-0.133-0.1420.0020.031-0.1230.3911.0000.259-0.130-0.069-0.1620.0830.083-0.047-0.112-0.100-0.1230.223
Wdspeed_Max-0.137-0.456-0.170-0.183-0.146-0.119-0.016-0.5980.8900.2591.000-0.146-0.133-0.1280.4020.4020.051-0.131-0.141-0.1210.725
humidity_avg0.0740.0790.4990.5030.444-0.204-0.1940.002-0.202-0.130-0.1461.0000.8960.8510.1170.1170.5540.1720.2290.135-0.040
humidity_m0.0640.0660.4030.4480.326-0.119-0.1200.016-0.161-0.069-0.1330.8961.0000.6300.0860.0860.4390.0870.1640.023-0.013
humidity_max0.0190.0480.4590.4250.466-0.331-0.283-0.043-0.199-0.162-0.1280.8510.6301.0000.1630.1630.5860.1970.2150.191-0.115
latitude1.0001.0000.0430.0440.0390.0000.2121.0000.3640.0830.4020.1170.0860.1631.0001.000-0.0060.0120.029-0.009-0.100
longitude1.0001.0000.0430.0440.0390.0000.2121.0000.3640.0830.4020.1170.0860.1631.0001.000-0.0070.0050.023-0.0140.300
precipitation_total-0.0000.0000.2090.1800.219-0.328-0.221-0.005-0.012-0.0470.0510.5540.4390.586-0.006-0.0071.0000.0300.0650.006-0.003
temperature_avg-0.019-0.0120.9320.9070.931-0.044-0.2800.005-0.160-0.112-0.1310.1720.0870.1970.0120.0050.0301.0000.9830.9860.005
temperature_m-0.026-0.0200.9410.9270.929-0.039-0.2820.013-0.163-0.100-0.1410.2290.1640.2150.0290.0230.0650.9831.0000.9490.018
temperature_max-0.013-0.0080.9040.8740.914-0.048-0.275-0.008-0.155-0.123-0.1210.1350.0230.191-0.009-0.0140.0060.9860.9491.0000.000
zipcode-0.100-0.500-0.012-0.007-0.0190.0690.064-0.6000.7490.2230.725-0.040-0.013-0.115-0.1000.300-0.0030.0050.0180.0001.000

Missing values

2023-11-28T11:25:15.843313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T11:25:16.124004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

StationlatitudelongitudeBoroughCityStatezipcodewdatetemperature_maxtemperature_avgtemperature_mDewpot_MaxDewpot_AvgDewpot_Mhumidity_maxhumidity_avghumidity_mWdspeed_MaxWdspeed_AvgWdspeed_MPressure_MaxPressure_Mprecipitation_totalUnnamed: 23Unnamed: 24Unnamed: 25
0KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0141.238.133.926.921.916.96051.04528.411.31.330.1029.960.0NaNNaNNaN
1KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0239.435.232.419.317.514.05648.04225.310.10.030.1129.950.0NaNNaNNaN
2KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0344.738.634.523.521.119.56049.03628.410.81.629.9729.780.0NaNNaNNaN
3KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0435.625.912.123.89.0-0.86248.03136.510.21.130.3929.860.0NaNNaNNaN
4KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0528.818.510.06.5-0.2-6.96445.02127.56.20.030.6430.370.0NaNNaNNaN
5KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0642.131.821.310.94.0-2.65332.01517.74.70.030.6030.410.0NaNNaNNaN
6KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0746.737.128.116.110.13.64933.01711.62.50.030.4230.180.0NaNNaNNaN
7KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0843.137.730.434.024.58.77259.03623.05.40.030.2630.160.0NaNNaNNaN
8KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-0944.742.238.243.037.331.09482.07125.76.50.030.2330.050.0NaNNaNNaN
9KNYBRONX1440.8616-73.8809BronxBotanical GardenNY104582016-01-1057.149.440.355.843.823.59783.04652.314.20.030.0629.292.0NaNNaNNaN
StationlatitudelongitudeBoroughCityStatezipcodewdatetemperature_maxtemperature_avgtemperature_mDewpot_MaxDewpot_AvgDewpot_Mhumidity_maxhumidity_avghumidity_mWdspeed_MaxWdspeed_AvgWdspeed_MPressure_MaxPressure_Mprecipitation_totalUnnamed: 23Unnamed: 24Unnamed: 25
10516KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-09-2873.967.561.266.460.450.59478.06812.01.30.029.8829.780.15NaNNaNNaN
10517KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-09-2969.461.452.550.546.342.37959.04212.01.40.030.0129.880.00NaNNaNNaN
10518KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-09-3066.961.255.450.047.645.07562.04711.00.90.030.1730.010.00NaNNaNNaN
10519KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0169.458.346.950.946.943.08868.04311.00.90.030.2530.160.01NaNNaNNaN
10520KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0276.862.650.058.653.147.19173.04615.01.30.030.1930.020.00NaNNaNNaN
10521KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0379.767.153.868.061.152.39782.0528.00.50.030.0329.880.00NaNNaN1.0
10522KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0475.268.862.669.365.960.89691.0807.00.20.030.0529.860.14NaNNaNNaN
10523KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0566.662.960.361.259.658.69589.0794.00.10.030.3130.040.03NaNNaNNaN
10524KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0670.365.261.962.260.458.39485.0745.00.10.030.3730.290.00NaNNaNNaN
10525KNYNEWYO10340.5674-74.1343Staten IslandRichmondtownNY103082021-10-0776.365.059.265.561.258.39888.0655.00.20.030.3130.180.00NaNNaNNaN